---
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: billm-mistral-7b-conll03-ner-maxlen-256
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# billm-mistral-7b-conll03-ner-maxlen-256

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2232
- Precision: 0.9277
- Recall: 0.9363
- F1: 0.9320
- Accuracy: 0.9863

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0329        | 1.0   | 7021  | 0.1599          | 0.9256    | 0.9357 | 0.9306 | 0.9856   |
| 0.0145        | 2.0   | 14042 | 0.1789          | 0.9312    | 0.9340 | 0.9326 | 0.9860   |
| 0.0106        | 3.0   | 21063 | 0.1931          | 0.9288    | 0.9359 | 0.9324 | 0.9864   |
| 0.0065        | 4.0   | 28084 | 0.2161          | 0.9277    | 0.9361 | 0.9319 | 0.9863   |
| 0.0043        | 5.0   | 35105 | 0.2168          | 0.9276    | 0.9363 | 0.9319 | 0.9864   |
| 0.002         | 6.0   | 42126 | 0.2250          | 0.9274    | 0.9359 | 0.9316 | 0.9863   |
| 0.0027        | 7.0   | 49147 | 0.2246          | 0.9269    | 0.9356 | 0.9312 | 0.9862   |
| 0.0023        | 8.0   | 56168 | 0.2235          | 0.9277    | 0.9364 | 0.9321 | 0.9863   |
| 0.0024        | 9.0   | 63189 | 0.2232          | 0.9276    | 0.9364 | 0.9320 | 0.9863   |
| 0.0016        | 10.0  | 70210 | 0.2232          | 0.9277    | 0.9363 | 0.9320 | 0.9863   |


### Framework versions

- PEFT 0.10.0
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1